The Decision Maker

In the December 2013 issue of the Harvard Business Review, reporting and analytics guru Thomas Davenport writes about how far the innovators in the analytics field have advanced.

Davenport divides the evolution of analytics into three eras.

Analytics 1.0 – This first era was marked by the first widespread use of data to support fact-based decision making. The idea of data warehouses became more main stream as enterprises began to organize their internal data specifically for the purpose of better analyzing historical data.

Many credit unions have just completed their strategic planning process, preparing them to navigate the many challenges ahead. I wonder how many credit unions ended up with one of their strategic priorities for 2014, a strategy of Improving Data. I wonder if the question of data and the complexities and opportunities present were even discussed. While it is clear that the volume of data is exploding and coming from different sources, it is not clear what questions should be asked.

The analytics research group has gathered thought leaders and industry experts to share its nine analytics predictions for 2014.

Evolving quickly alongside today’s business demands, the data scientist profession is faced with the challenges of interdisciplinary rarity. And the job pool is struggling to keep up. Just as we’re beginning to put Big Data in its place, its human counterpart seeks definition within the modern enterprise.

Can the data scientist keep up with today’s growing data demands? The specialized and expensive skills required for this coveted profession are hard to find, making the data scientist a difficult role to scale. So even as the data scientist is hailed as the hero to optimize business as we know it, automation must assist this practice in efficiency.

Preparing to Compete

A data warehouse is a database used for reporting and data analysis. It is a central repository of data which is created by integrating data from one or more different sources. Data warehouses store current and historical data and are used for creating trending reports for various levels of management.

Many credit unions are launching business intelligence initiatives with high hopes for improving performance. However there are two big challenges to overcome.

First, important data is usually bottled up in unrelated “silos”. It is essential to build a universal data model to connect all the various data sources into a cohesive repository. This becomes the “single source of truth” on which all subsequent analytical efforts depend.

Second, an effective analytics capability needs to be developed. This requires careful consideration. A recent Forbes magazine article provides some great insight to those taking on the second challenge.

“We don’t have enough data to make good decisions! “ Have you heard a similar complaint at your office? It may be a valid frustration but the root cause of the problem is off the mark.

The details of the problem are nicely captured in an article by Lawrence Buettner of Wausau Financial Systems. The following are an interpretation of his conclusions as they apply to community financial institutions (CFIs):

1. CFIs are awash in data but they are unable to use it effectively
2. They know they need help with these data problems but don’t know where to begin
3. They perceive a difficulty in balancing the transaction needs of customers and the analytical needs of internal management

Risk and compliance are often at the top of the list when it comes to headaches for community financial institutions. Yet, an underperforming risk and compliance management program can be a dire threat. What factors separate high-performing programs from underachievers?